Dynamics of a transitional river pattern : a multi-scale investigation of controls on the wandering pattern of Miramichi rivers, New Brunswick, Canada
Bibliographic record
Abstract
The wandering river pattern represents one of the last remaining river patterns that are not well understood. Many aspects of these rivers are not well known, particularly the processes of their creation and maintenance. The term wandering describes gravel or cobble bedded rivers, transitional between braided and meandering, with multiple channel sections around semi-permanent islands connected by single channel sections. This dissertation investigates the controls on the characteristics of wandering rivers within the Miramichi region of New Brunswick through time and at three nested spatial scales. At the scale of rivers, three factors appear to be needed for wandering to occur: (1) wide valleys, (2) channel energy between braiding and meandering, and (3) avulsion triggers, frequent overbank flows caused by icejams in the Miramichi. Principal component analysis showed that larger wandering rivers displayed greater anabranching intensity than smaller rivers, perhaps related to higher stage ice jams within larger rivers. At the scale of channels, the wandering pattern of the Renous River was found to be in a state of dynamic equilibrium, with channel creation balanced by channel abandonment. The anabranch cycle model was developed to illustrate the temporal dynamics of anabranch creation, maintenance and abandonment within wandering rivers. Also at the channel scale, principal component analysis of channel reaches within the Renous River displayed differences in grain size and hydraulic efficiency between side-channels and main-channels. Energy and sediment mobility within side-channels was related to their formation, maintenance and abandonment. Energy and sediment mobility within main-channels was related to mega bedforms called bedwaves. The apex of some bedwaves occurred at diffluences. At the scale of channel elements, diffluences are stable where a large bar is formed and accretes upstream, creating a large reservoir of sediment upstream of anabranch channels to buffer their degradation. Where diffluences are unstable, a large bar forms within one anabranch channel to partially block flow and may cause its abandonment. The dissertation illustrates that within wandering rivers, processes occurring at multiple spatial and temporal scales interact to create and maintain the pattern.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".